Longitudinal Data Analysis

Course Description

Studies in which data are collected repeatedly on a sample of individuals over time are common in the health, social, and behavioral sciences; agricultural and biological sciences; education; economics; and business. Questions of interest in the context of such longitudinal data often focus on patterns of change of outcomes of interest over time and on identifying factors that are associated with patterns of change in relevant populations of individuals.This course covers statistical models for drawing scientific inferences from longitudinal data. Topics include: longitudinal study design, exploring longitudinal data, linear and general linear regression models for correlated data, including marginal, random effects and transition models, and handling missing data.

Course Objective

       To provide theoretical concepts on fundamental statistical models and methods for the analysis of longitudinal data, and their implementation using SAS and R by considering real datasets.

 Prerequisite

•         Basic understanding on Generalized linear Models

•         Familiarity with SAS

-          How to read data from a file

-          How perform simple data manipulations

-          Basic use of simple procedures such as PROC GLM.

•         Familiarity with R

-          Data management in R

-          Installing packages (i.e. nlme,lme4,)

-          Fitting standard GLM models

NOTE: Lecture slides, projects, datasets and codes, and reference books and/or reading material are available in the course webpage.